initial :fire:

app.py

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+from matplotlib.pyplot import axis
+import gradio as gr
+import requests
+import numpy as np
+from torch import nn
+from transformers import SegformerFeatureExtractor, SegformerForSemanticSegmentation
+import requests
+
+import torch
+
+from detectron2 import model_zoo
+from detectron2.engine import DefaultPredictor
+from detectron2.config import get_cfg
+from detectron2.utils.visualizer import Visualizer
+from detectron2.data import MetadataCatalog
+
+
+url1 = 'https://cdn.pixabay.com/photo/2014/09/07/21/52/city-438393_1280.jpg'
+r = requests.get(url1, allow_redirects=True)
+open("city1.jpg", 'wb').write(r.content)
+url2 = 'https://cdn.pixabay.com/photo/2016/02/19/11/36/canal-1209808_1280.jpg'
+r = requests.get(url2, allow_redirects=True)
+open("city2.jpg", 'wb').write(r.content)
+
+model_name='COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x.yaml'
+
+# model = model_zoo.get(model_name, trained=True)
+
+cfg = get_cfg()
+# add project-specific config (e.g., TensorMask) here if you're not running a model in detectron2's core library
+cfg.merge_from_file(model_zoo.get_config_file(model_name))
+cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5  # set threshold for this model
+# Find a model from detectron2's model zoo. You can use the https://dl.fbaipublicfiles... url as w ell
+cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url(model_name)
+
+if not torch.cuda.is_available():
+    cfg.MODEL.DEVICE='cpu'
+
+predictor = DefaultPredictor(cfg)
+
+
+def inference(image):
+    img = np.array(image.resize((1024,1024)))
+    outputs = predictor(img)
+
+    v = Visualizer(img, MetadataCatalog.get(cfg.DATASETS.TRAIN[0]), scale=1.2)
+    out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
+    
+    return out.get_image()
+
+
+
+title = "Detectron2-MaskRCNN X101"
+description = "demo for Detectron2. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below.\
+    </br><b>Model: COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x.yaml</b>"
+article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2012.07177'>Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation</a> | <a href='https://github.com/facebookresearch/detectron2/blob/main/MODEL_ZOO.md'>Detectron model ZOO</a></p>"
+
+gr.Interface(
+    inference, 
+    [gr.inputs.Image(type="pil", label="Input")], 
+    gr.outputs.Image(type="numpy", label="Output"),
+    title=title,
+    description=description,
+    article=article,
+    examples=[
+            ["city1.jpg"],
+            ["city2.jpg"]
+        ]).launch()

requirements.txt

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+opencv-python-headless
+pyyaml==5.1
+torch
+git+https://github.com/facebookresearch/detectron2.git